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Automated phonological analysis and treatment target selection using AutoPATT.

Philip Combiths1,2, Ray Amberg3, Gregory Hedlund4

  • 1School of Speech, Language, and Hearing Sciences, San Diego State University, San Diego, California, USA.

Clinical Linguistics & Phonetics
|June 4, 2021
PubMed
Summary
This summary is machine-generated.

AutoPATT, an automated speech analysis tool, offers more accurate phonetic and phonemic inventory assessments for children with speech sound disorders than manual methods. Clinical judgment remains essential for treatment target selection.

Keywords:
Phonologyautomated assessmentphonological disorder

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Area of Science:

  • Speech-Language Pathology
  • Computational Linguistics
  • Pediatric Audiology

Background:

  • Speech sound disorders (SSDs) affect many children, necessitating efficient and accurate diagnostic tools.
  • Current clinical assessment of SSDs can be time-consuming and prone to variability.
  • Automated speech analysis offers potential for improved accuracy and efficiency in clinical settings.

Purpose of the Study:

  • To introduce AutoPATT, a novel, free, open-source automated tool for the clinical analysis of speech samples.
  • To evaluate the accuracy of AutoPATT's inventory analyses and treatment target suggestions compared to manual methods.
  • To streamline the assessment process for children with suspected speech sound disorders.

Main Methods:

  • AutoPATT was developed as a plug-in for Phon, adhering to the Phonological Analysis and Treatment Target Selection protocol.
  • The tool extracts phonetic inventory, phonemic inventory with minimal pairs, and initial consonant cluster inventory.
  • Automated analyses and target suggestions were compared against manual analyses of 25 speech samples from children with phonological disorder.

Main Results:

  • AutoPATT demonstrated superior accuracy in phonetic and phonemic inventory analyses compared to manual assessments.
  • The tool successfully extracted key phonological data, including minimal pairs and consonant clusters.
  • Treatment target suggestions provided by AutoPATT require careful clinical review.

Conclusions:

  • AutoPATT represents a significant advancement in the automated analysis of speech samples for children with SSDs.
  • The tool enhances accuracy in inventory assessment, potentially saving clinical time.
  • While valuable for suggestions, AutoPATT should complement, not replace, clinical expertise in treatment planning.